[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121141-en":3,"doc-seo-121141-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121141,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps","Hybrid Quantum-Classical (HQC) architectures drive near-term NISQ quantum machine learning by leveraging quantum embeddings and variational circuits to target measurable performance gains over classical computation. A key implementation challenge lies in choosing functionally correct quantum embeddings and building training-ready quantum variational circuits. This work presents a QSVM-based approach to predict future mental-health treatment needs for individuals in the tech domain using OSMI survey data. The study uses non-classically simulable feature maps and argues that NISQ HQC models can yield strong real-world predictive performance.","Quantum Machine Learning with HQC Architectures using non-Classically Simulable  \nFeature Maps  \nSyed Farhan Ahmad1,2,  \n1. Department of Electronics and Communication Engineering, RVCE, Bangalore, Karnataka, India 2. QPower Research, Salt Lake City, Utah, United States  \n[syedfarhana.ec18@rvce.edu.in](syedfarhana.ec18@rvce.edu.in)  \nRaghav Rawat1,2,  \n1. Department of Electronics and Communication Engineering, RVCE, Bangalore, Karnataka, India 2. QPower Research, Salt Lake City, Utah, United States  \n[raghavrawat.ec18@rvce.edu.in](raghavrawat.ec18@rvce.edu.in)  \nMinal Moharir  \nDepartment of Computer Science and Engineering, RVCE, Bangalore, Karnataka, India  \n[minalmoharir@rvce.edu.in](minalmoharir@rvce.edu.in)  \nAbstract— Hybrid Quantum-Classical (HQC) Architectures are used in near-term NISQ Quantum Computers for solving Quantum Machine Learning problems. The quantum advantage comes into picture due to the exponential speedup offered over classical computing. One of the major challenges in implementing such algorithms is the choice of quantum embeddings and the use of a functionally correct quantum variational circuit. In this paper, we present an application of QSVM (Quantum Support Vector Machines) to predict if a person will require mental health treatment in the tech world in the future using the dataset from OSMI Mental Health Tech Surveys. We achieve this with non-classically simulable feature maps and prove that NISQ HQC Architectures for Quantum Machine Learning can be used alternatively to create good performance models in near-term real-world applications.  \nKeywords— Quantum Machine Learning, Hybrid QuantumClassical Architecture, QSVM, Quantum Feature Map, Quantum binary Classifiers  \nI. INTRODUCTION  \nQuantum Computing is the use of Quantum physics and Quantum phenomena for computation of tasks. It is a completely different paradigm and has shown great applications for problems that Classical Computers cannot solve. The hype and scepticism of Quantum Computers were positively justified when Google successfully demonstrated Quantum Supremacy with their 54-qubit Sycamore processor [ 1] . Quantum Computing has multiple real-life applications that include Drug discovery [2], Disease Risk Predictions [2], Routing and Optimization problems [3], Materials Discovery [4], and Machine Learning[ 19] .  \nQuantum Computing can greatly help in creating better Machine Learning models[ 16] that train much faster and can encode more information than their classical counterparts[ 18] . They provide an exponential speedup due to the availability of an exponential state space in which the classical data can be mapped [5]. The field of Quantum Machine Learning has seen the use of 3 different architectures: Quantum Data with Classical processing, that can be used for phase estimations in matter [6], Quantum data with Quantum processing, that can be used for inherent quantum error correction [7], and the final architecture is the where classical data is mapped onto quantum systems and the power of quantum mechanics helps in solving the problem faster and with a better accuracy(in some cases) [3] . This architecture is known as the HQC (Hybrid Quantum-Classical) architecture. In this paper, we  \nwill be discussing about a Quantum Classifier as a trainable quantum circuit which is a form of HQC architecture for quantum machine learning.  \nToday’s generation of Quantum Computers fall under the NISQ (Noisy Intermediate-State Quantum) era, which makes them prone to errors, less reliable and not very usable[ 17] . Measurement errors, interactions of adjacent qubits and thermal fluctuations are the main causes by which fidelity of quantum gates and circuits are greatly reduced. The Hybrid Quantum-Classical Architecture for Machine Learning shows promising results in this NISQ era [8] .  \nThe quantum component ofthe HQC Architecture maps data features very effectively compared to Classical SVM (Support Vector Machines) Architecture and in th","cbCaiuUNFLPk1Ude","https://ap.wps.com/l/cbCaiuUNFLPk1Ude","pdf",1025275,1,5,"English","en",105,"# Introduction\n## Quantum computing and quantum machine learning overview\n## NISQ constraints and HQC motivation\n## Quantum feature maps and kernel embedding\n## QSVM classification workflow","[{\"question\":\"What challenge does the paper identify for hybrid quantum-classical quantum machine learning implementations?\",\"answer\":\"The paper highlights the difficulty of selecting suitable quantum embeddings and constructing a functionally correct quantum variational circuit for training.\"},{\"question\":\"How does the proposed method use QSVM for prediction?\",\"answer\":\"It applies a quantum support vector machine (QSVM) as a supervised quantum binary classifier to predict whether a person will require mental health treatment in the tech world in the future.\"},{\"question\":\"Why are non-classically simulable feature maps important in the approach?\",\"answer\":\"They enable a classical-to-quantum mapping that cannot be simulated classically, aiming to provide quantum advantage via quantum effects such as entanglement.\"}]","Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps | 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challenge does the paper identify for hybrid quantum-classical quantum machine learning implementations?","Question",{"text":75,"@type":76},"The paper highlights the difficulty of selecting suitable quantum embeddings and constructing a functionally correct quantum variational circuit for training.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method use QSVM for prediction?",{"text":80,"@type":76},"It applies a quantum support vector machine (QSVM) as a supervised quantum binary classifier to predict whether a person will require mental health treatment in the tech world in the future.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are non-classically simulable feature maps important in the approach?",{"text":84,"@type":76},"They enable a classical-to-quantum mapping that cannot be simulated classically, aiming to provide quantum advantage via quantum effects such as 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